Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisis
Massive parallel DNA sequencing combined with chromatin immunoprecipitation and a large variety of DNA/RNA-enrichment methodologies is at the origin of data resources of major importance. Indeed these resources, available for multiple genomes, represent the most comprehensive catalogue of (i) cell,...
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| Format: | Article |
| Language: | English |
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Elsevier
2014-12-01
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| Series: | Genomics Data |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213596014000671 |
| _version_ | 1818893752845991936 |
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| author | Marco Antonio Mendoza-Parra Hinrich Gronemeyer |
| author_facet | Marco Antonio Mendoza-Parra Hinrich Gronemeyer |
| author_sort | Marco Antonio Mendoza-Parra |
| collection | DOAJ |
| description | Massive parallel DNA sequencing combined with chromatin immunoprecipitation and a large variety of DNA/RNA-enrichment methodologies is at the origin of data resources of major importance. Indeed these resources, available for multiple genomes, represent the most comprehensive catalogue of (i) cell, development and signal transduction-specified patterns of binding sites for transcription factors (‘cistromes’) and for transcription and chromatin modifying machineries and (ii) the patterns of specific local post-translational modifications of histones and DNA (‘epigenome’) or of regulatory chromatin binding factors. In addition, (iii) the resources specifying chromatin structure alterations are emerging. Importantly, these types of “omics” datasets populate increasingly public repositories and provide highly valuable resources for the exploration of general principles of cell function in a multi-dimensional genome–transcriptome–epigenome–chromatin structure context. However, data mining is critically dependent on the data quality, an issue that, surprisingly, is still largely ignored by scientists and well-financed consortia, data repositories and scientific journals. So what determines the quality of ChIP-seq experiments and the datasets generated therefrom and what refrains scientists from associating quality criteria to their data? In this ‘opinion’ we trace the various parameters that influence the quality of this type of datasets, as well as the computational efforts that were made until now to qualify them. Moreover, we describe a universal quality control (QC) certification approach that provides a quality rating for ChIP-seq and enrichment-related assays. The corresponding QC tool and a regularly updated database, from which at present the quality parameters of more than 8000 datasets can be retrieved, are freely accessible at www.ngs-qc.org. |
| first_indexed | 2024-12-19T18:17:36Z |
| format | Article |
| id | doaj.art-7f2a8a441eea40bd960f91ade7b1f030 |
| institution | Directory Open Access Journal |
| issn | 2213-5960 |
| language | English |
| last_indexed | 2024-12-19T18:17:36Z |
| publishDate | 2014-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Genomics Data |
| spelling | doaj.art-7f2a8a441eea40bd960f91ade7b1f0302022-12-21T20:11:03ZengElsevierGenomics Data2213-59602014-12-012C26827310.1016/j.gdata.2014.08.002Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisisMarco Antonio Mendoza-ParraHinrich GronemeyerMassive parallel DNA sequencing combined with chromatin immunoprecipitation and a large variety of DNA/RNA-enrichment methodologies is at the origin of data resources of major importance. Indeed these resources, available for multiple genomes, represent the most comprehensive catalogue of (i) cell, development and signal transduction-specified patterns of binding sites for transcription factors (‘cistromes’) and for transcription and chromatin modifying machineries and (ii) the patterns of specific local post-translational modifications of histones and DNA (‘epigenome’) or of regulatory chromatin binding factors. In addition, (iii) the resources specifying chromatin structure alterations are emerging. Importantly, these types of “omics” datasets populate increasingly public repositories and provide highly valuable resources for the exploration of general principles of cell function in a multi-dimensional genome–transcriptome–epigenome–chromatin structure context. However, data mining is critically dependent on the data quality, an issue that, surprisingly, is still largely ignored by scientists and well-financed consortia, data repositories and scientific journals. So what determines the quality of ChIP-seq experiments and the datasets generated therefrom and what refrains scientists from associating quality criteria to their data? In this ‘opinion’ we trace the various parameters that influence the quality of this type of datasets, as well as the computational efforts that were made until now to qualify them. Moreover, we describe a universal quality control (QC) certification approach that provides a quality rating for ChIP-seq and enrichment-related assays. The corresponding QC tool and a regularly updated database, from which at present the quality parameters of more than 8000 datasets can be retrieved, are freely accessible at www.ngs-qc.org.http://www.sciencedirect.com/science/article/pii/S2213596014000671ChIP sequencingMassive parallel sequencingQuality controlOmics data mining |
| spellingShingle | Marco Antonio Mendoza-Parra Hinrich Gronemeyer Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisis Genomics Data ChIP sequencing Massive parallel sequencing Quality control Omics data mining |
| title | Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisis |
| title_full | Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisis |
| title_fullStr | Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisis |
| title_full_unstemmed | Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisis |
| title_short | Assessing quality standards for ChIP-seq and related massive parallel sequencing-generated datasets: When rating goes beyond avoiding the crisis |
| title_sort | assessing quality standards for chip seq and related massive parallel sequencing generated datasets when rating goes beyond avoiding the crisis |
| topic | ChIP sequencing Massive parallel sequencing Quality control Omics data mining |
| url | http://www.sciencedirect.com/science/article/pii/S2213596014000671 |
| work_keys_str_mv | AT marcoantoniomendozaparra assessingqualitystandardsforchipseqandrelatedmassiveparallelsequencinggenerateddatasetswhenratinggoesbeyondavoidingthecrisis AT hinrichgronemeyer assessingqualitystandardsforchipseqandrelatedmassiveparallelsequencinggenerateddatasetswhenratinggoesbeyondavoidingthecrisis |